COVID-19 Interactive Analysis Dashboard

# importing libraries

from __future__ import print_function
from ipywidgets import interact, interactive, fixed, interact_manual
from IPython.core.display import display, HTML

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly.express as px
import folium
import plotly.graph_objects as go
import seaborn as sns
import ipywidgets as widgets

# loading data right from the source:
death_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv')
confirmed_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv')
recovered_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv')
country_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/web-data/data/cases_country.csv')

confirmed_df.head()

	Province/State	Country/Region	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	...	11/15/20	11/16/20	11/17/20	11/18/20	11/19/20	11/20/20	11/21/20	11/22/20	11/23/20	11/24/20
0	NaN	Afghanistan	33.93911	67.709953	0	0	0	0	0	0	...	43240	43403	43628	43851	44228	44443	44503	44706	44988	45280
1	NaN	Albania	41.15330	20.168300	0	0	0	0	0	0	...	27830	28432	29126	29837	30623	31459	32196	32761	33556	34300
2	NaN	Algeria	28.03390	1.659600	0	0	0	0	0	0	...	67679	68589	69591	70629	71652	72755	73774	74862	75867	77000
3	NaN	Andorra	42.50630	1.521800	0	0	0	0	0	0	...	5872	5914	5951	6018	6066	6142	6207	6256	6304	6351
4	NaN	Angola	-11.20270	17.873900	0	0	0	0	0	0	...	13451	13615	13818	13922	14134	14267	14413	14493	14634	14742

5 rows × 312 columns

recovered_df.head()

	Province/State	Country/Region	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	...	11/15/20	11/16/20	11/17/20	11/18/20	11/19/20	11/20/20	11/21/20	11/22/20	11/23/20	11/24/20
0	NaN	Afghanistan	33.93911	67.709953	0	0	0	0	0	0	...	35092	35137	35160	35295	35350	35370	35422	35934	35976	36122
1	NaN	Albania	41.15330	20.168300	0	0	0	0	0	0	...	12889	13453	13804	14216	14565	15055	15469	15842	16230	16666
2	NaN	Algeria	28.03390	1.659600	0	0	0	0	0	0	...	44633	45148	45148	46326	46962	47581	48183	48794	49421	50070
3	NaN	Andorra	42.50630	1.521800	0	0	0	0	0	0	...	4747	4830	4965	5055	5127	5239	5290	5358	5405	5503
4	NaN	Angola	-11.20270	17.873900	0	0	0	0	0	0	...	6444	6523	6582	6623	7062	7117	7273	7346	7351	7444

5 rows × 312 columns

death_df.head()

	Province/State	Country/Region	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	...	11/15/20	11/16/20	11/17/20	11/18/20	11/19/20	11/20/20	11/21/20	11/22/20	11/23/20	11/24/20
0	NaN	Afghanistan	33.93911	67.709953	0	0	0	0	0	0	...	1617	1626	1638	1645	1650	1661	1675	1687	1695	1712
1	NaN	Albania	41.15330	20.168300	0	0	0	0	0	0	...	623	631	637	646	657	672	685	699	716	735
2	NaN	Algeria	28.03390	1.659600	0	0	0	0	0	0	...	2154	2168	2186	2206	2224	2236	2255	2272	2294	2309
3	NaN	Andorra	42.50630	1.521800	0	0	0	0	0	0	...	76	76	76	76	76	76	76	76	76	76
4	NaN	Angola	-11.20270	17.873900	0	0	0	0	0	0	...	322	324	328	332	333	334	336	337	337	338

5 rows × 312 columns

country_df.head()

	Country_Region	Last_Update	Lat	Long_	Confirmed	Deaths	Recovered	Active	Incident_Rate	People_Tested	People_Hospitalized	Mortality_Rate	UID	ISO3
0	Afghanistan	2020-11-25 12:26:29	33.93911	67.709953	45490.0	1725.0	36145.0	7620.0	116.855738	NaN	NaN	3.792042	4	AFG
1	Albania	2020-11-25 12:26:29	41.15330	20.168300	34300.0	735.0	16666.0	16899.0	1191.882688	NaN	NaN	2.142857	8	ALB
2	Algeria	2020-11-25 12:26:29	28.03390	1.659600	77000.0	2309.0	50070.0	24621.0	175.594455	NaN	NaN	2.998701	12	DZA
3	Andorra	2020-11-25 12:26:29	42.50630	1.521800	6351.0	76.0	5503.0	772.0	8219.763153	NaN	NaN	1.196662	20	AND
4	Angola	2020-11-25 12:26:29	-11.20270	17.873900	14742.0	338.0	7444.0	6960.0	44.854499	NaN	NaN	2.292769	24	AGO

# data cleaning

# renaming the df column names to lowercase
country_df.columns = map(str.lower, country_df.columns)
confirmed_df.columns = map(str.lower, confirmed_df.columns)
death_df.columns = map(str.lower, death_df.columns)
recovered_df.columns = map(str.lower, recovered_df.columns)

# changing province/state to state and country/region to country
confirmed_df = confirmed_df.rename(columns={'province/state': 'state', 'country/region': 'country'})
recovered_df = confirmed_df.rename(columns={'province/state': 'state', 'country/region': 'country'})
death_df = death_df.rename(columns={'province/state': 'state', 'country/region': 'country'})
country_df = country_df.rename(columns={'country_region': 'country'})
# country_df.head()

# total number of confirmed, death and recovered cases
confirmed_total = int(country_df['confirmed'].sum())
deaths_total = int(country_df['deaths'].sum())
recovered_total = int(country_df['recovered'].sum())
active_total = int(country_df['active'].sum())

# displaying the total stats

display(HTML("<div style = 'background-color: #090C02; padding: 30px '>" +
             "<span style='color: #FFE5D9; font-size:30px;'> Confirmed: "  + str(confirmed_total) +"</span>" +
             "<span style='color: #ED1C24; font-size:30px;margin-left:20px;'> Deaths: " + str(deaths_total) + "</span>"+
             "<span style='color: #D8E2DC; font-size:30px; margin-left:20px;'> Recovered: " + str(recovered_total) + "</span>"+
             "</div>")
       )

Confirmed: 59905468 Deaths: 1412223 Recovered: 38377692

# sorting the values by confirmed descednding order
# country_df.sort_values('confirmed', ascending= False).head(10).style.background_gradient(cmap='copper')
fig = go.FigureWidget( layout=go.Layout() )
def highlight_col(x):
    r = 'background-color: red'
    y = 'background-color: purple'
    g = 'background-color: grey'
    df1 = pd.DataFrame('', index=x.index, columns=x.columns)
    df1.iloc[:, 4] = y
    df1.iloc[:, 5] = r
    df1.iloc[:, 6] = g
    
    return df1

def show_latest_cases(n):
    n = int(n)
    return country_df.sort_values('confirmed', ascending= False).head(n).style.apply(highlight_col, axis=None)

interact(show_latest_cases, n='10')

ipywLayout = widgets.Layout(border='solid 2px green')
ipywLayout.display='none' # uncomment this, run cell again - then the graph/figure disappears
widgets.VBox([fig], layout=ipywLayout)

	country	last_update	lat	long_	confirmed	deaths	recovered	active	incident_rate	people_tested	people_hospitalized	mortality_rate	uid	iso3
176	US	2020-11-25 12:26:29	40.000000	-100.000000	12598660.000000	259976.000000	4696664.000000	7642011.000000	3823.960341	nan	nan	2.063521	840	USA
79	India	2020-11-25 12:26:29	20.593684	78.962880	9222216.000000	134699.000000	8642771.000000	444746.000000	668.274398	nan	nan	1.460593	356	IND
23	Brazil	2020-11-25 12:26:29	-14.235000	-51.925300	6118708.000000	170115.000000	5509645.000000	438948.000000	2878.587228	nan	nan	2.780244	76	BRA
62	France	2020-11-25 12:26:29	46.227600	2.213700	2206126.000000	50327.000000	160257.000000	1994750.000000	3379.818141	nan	nan	2.281239	250	FRA
141	Russia	2020-11-25 12:26:29	61.524000	105.318800	2144229.000000	37173.000000	1646737.000000	460319.000000	1469.309579	nan	nan	1.733630	643	RUS
160	Spain	2020-11-25 12:26:29	40.463667	-3.749220	1594844.000000	43668.000000	150376.000000	1400800.000000	3411.082028	nan	nan	2.738073	724	ESP
180	United Kingdom	2020-11-25 12:26:29	55.000000	-3.000000	1542623.000000	55935.000000	3410.000000	1483278.000000	2272.372668	nan	nan	3.625967	826	GBR
85	Italy	2020-11-25 12:26:29	41.871900	12.567400	1455022.000000	51306.000000	605330.000000	798386.000000	2406.513412	nan	nan	3.526132	380	ITA
6	Argentina	2020-11-25 12:26:29	-38.416100	-63.616700	1381795.000000	37432.000000	1210634.000000	133729.000000	3057.354230	nan	nan	2.708940	32	ARG
37	Colombia	2020-11-25 12:26:29	4.570900	-74.297300	1262494.000000	35677.000000	1167857.000000	58960.000000	2481.176185	nan	nan	2.825914	170	COL

sorted_country_df = country_df.sort_values('confirmed', ascending= False)

Slide to check for the worst hit countries

# # plotting the 20 worst hit countries

def bubble_chart(n):
    fig = px.scatter(sorted_country_df.head(n), x="country", y="confirmed", size="confirmed", color="country",
               hover_name="country", size_max=60)
    fig.update_layout(
    title=str(n) +" Worst hit countries",
    xaxis_title="Countries",
    yaxis_title="Confirmed Cases",
    width = 700
    )
    fig.show();

interact(bubble_chart, n=10)

ipywLayout = widgets.Layout(border='solid 2px green')
ipywLayout.display='none'
widgets.VBox([fig], layout=ipywLayout)

def plot_cases_of_a_country(country):
    labels = ['confirmed', 'deaths']
    colors = ['blue', 'red']
    mode_size = [6, 8]
    line_size = [4, 5]
    
    df_list = [confirmed_df, death_df]
    
    fig = go.Figure();
    
    for i, df in enumerate(df_list):
        if country == 'World' or country == 'world':
            x_data = np.array(list(df.iloc[:, 20:].columns))
            y_data = np.sum(np.asarray(df.iloc[:,4:]),axis = 0)
            
        else:    
            x_data = np.array(list(df.iloc[:, 20:].columns))
            y_data = np.sum(np.asarray(df[df['country'] == country].iloc[:,20:]),axis = 0)
            
        fig.add_trace(go.Scatter(x=x_data, y=y_data, mode='lines+markers',
        name=labels[i],
        line=dict(color=colors[i], width=line_size[i]),
        connectgaps=True,
        text = "Total " + str(labels[i]) +": "+ str(y_data[-1])
        ));
    
    fig.update_layout(
        title="COVID 19 cases of " + country,
        xaxis_title='Date',
        yaxis_title='No. of Confirmed Cases',
        margin=dict(l=20, r=20, t=40, b=20),
        paper_bgcolor="lightgrey",
        width = 800,
        
    );
    
    fig.update_yaxes(type="linear")
    fig.show();

Check the details of your country or the World

• Enter the name of your country(in capitalized format(e.g. Italy)) and world for total cases

interact(plot_cases_of_a_country, country='World')

ipywLayout = widgets.Layout(border='solid 2px green')
ipywLayout.display='none' # uncomment this, run cell again - then the graph/figure disappears
widgets.VBox([fig], layout=ipywLayout)

10 worst hit countries - Confirmed cases

px.bar(
    sorted_country_df.head(10),
    x = "country",
    y = "confirmed",
    title= "Top 10 worst affected countries", # the axis names
    color_discrete_sequence=["pink"], 
    height=500,
    width=800
)

10 worst hit countries - Death cases

px.bar(
    sorted_country_df.head(10),
    x = "country",
    y = "deaths",
    title= "Top 10 worst affected countries", # the axis names
    color_discrete_sequence=["pink"], 
    height=500,
    width=800
)

Worst hit countries - Recovering cases

px.bar(
    sorted_country_df.head(10),
    x = "country",
    y = "recovered",
    title= "Top 10 worst affected countries", # the axis names
    color_discrete_sequence=["pink"], 
    height=500,
    width=800
)

Global spread of COVID-19

import math
world_map = folium.Map(location=[11,0], tiles="cartodbpositron", zoom_start=2, max_zoom = 6, min_zoom = 2)


for i in range(0,len(confirmed_df)):
    if (-360<confirmed_df.iloc[i]['lat']<360 and -360<confirmed_df.iloc[i]['long']<360 ):
      folium.Circle(
        location=[confirmed_df.iloc[i]['lat'], confirmed_df.iloc[i]['long']],
        fill=True,
        radius=(int((np.log(confirmed_df.iloc[i,-1]+1.00001)))+0.2)*50000,
        color='red',
        fill_color='indigo',
        tooltip = "<div style='margin: 0; background-color: black; color: white;'>"+
                    "<h4 style='text-align:center;font-weight: bold'>"+confirmed_df.iloc[i]['country'] + "</h4>"
                    "<hr style='margin:10px;color: white;'>"+
                    "<ul style='color: white;;list-style-type:circle;align-item:left;padding-left:20px;padding-right:20px'>"+
                        "<li>Confirmed: "+str(confirmed_df.iloc[i,-1])+"</li>"+
                        "<li>Deaths:   "+str(death_df.iloc[i,-1])+"</li>"+
                        "<li>Death Rate: "+ str(np.round(death_df.iloc[i,-1]/(confirmed_df.iloc[i,-1]+1.00001)*100,2))+ "</li>"+
                    "</ul></div>",
        ).add_to(world_map)

world_map

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